


import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase     		=							logging.get_logger(__name__)


__UpperCamelCase     		=							'https://openaipublic.azureedge.net/jukebox/models/'
__UpperCamelCase     		=							{
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}
def 							UpperCamelCase_(					_A :Optional[int]	)->		Optional[Any]:
			if key.endswith(".model.1.bias"	) and len(key.split("."	)	) > 10:
						UpperCamelCase__       =							key.replace(".model.1.bias"					,  ".conv1d_1.bias"	)
			elif key.endswith(".model.1.weight"	) and len(key.split("."	)	) > 10:
						UpperCamelCase__       =							key.replace(".model.1.weight"					,  ".conv1d_1.weight"	)
			elif key.endswith(".model.3.bias"	) and len(key.split("."	)	) > 10:
						UpperCamelCase__       =							key.replace(".model.3.bias"					,  ".conv1d_2.bias"	)
			elif key.endswith(".model.3.weight"	) and len(key.split("."	)	) > 10:
						UpperCamelCase__       =							key.replace(".model.3.weight"					,  ".conv1d_2.weight"	)

			if "conditioner_blocks.0." in key:
						UpperCamelCase__       =							key.replace("conditioner_blocks.0"					,  "conditioner_blocks"	)

			if "prime_prior" in key:
						UpperCamelCase__       =							key.replace("prime_prior"					,  "encoder"	)

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						UpperCamelCase__       =							key.replace(".emb."					,  "."	)

			if key.endswith("k"	):  # replace vqvae.X.k with vqvae.X.codebook
						return key.replace(".k"					,  ".codebook"	)
			if "y_emb." in key:
						return key.replace("y_emb."					,  "metadata_embedding."	)

			if "x_emb.emb." in key:
						UpperCamelCase__       =							key.replace("0.x_emb.emb"					,  "embed_tokens"	)

			if "prime_state_ln" in key:
						return key.replace("prime_state_ln"					,  "encoder.final_layer_norm"	)
			if ".ln" in key:
						return key.replace(".ln"					,  ".layer_norm"	)
			if "_ln" in key:
						return key.replace("_ln"					,  "_layer_norm"	)

			if "prime_state_proj" in key:
						return key.replace("prime_state_proj"					,  "encoder.proj_in"	)
			if "prime_x_out" in key:
						return key.replace("prime_x_out"					,  "encoder.lm_head"	)
			if "prior.x_out" in key:
						return key.replace("x_out"					,  "fc_proj_out"	)
			if "x_emb" in key:
						return key.replace("x_emb"					,  "embed_tokens"	)

			return key
def 							UpperCamelCase_(					_A :str					,  _A :List[Any]					,  _A :Optional[int]					,  _A :str	)->		Any:
			UpperCamelCase__       =							{}
			import re

			UpperCamelCase__       =							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(
			    r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"	)

			UpperCamelCase__       =							re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(
			    r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"	)

			UpperCamelCase__       =							re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(
			    r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"	)
			UpperCamelCase__       =							re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"	)

			for original_key, value in state_dict.items():
						# rename vqvae.encoder keys
						if re_encoder_block_conv_in.fullmatch(_A	):
									UpperCamelCase__       =							re_encoder_block_conv_in.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[2]	) * 2 + int(groups[3]	)
									UpperCamelCase__       =							F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
									UpperCamelCase__       =							re_encoder_block_conv_in.sub(_A					,  _A	)

						elif re_encoder_block_resnet.fullmatch(_A	):
									UpperCamelCase__       =							re_encoder_block_resnet.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[2]	) * 2 + int(groups[3]	)
									UpperCamelCase__       =							{"1": 1, "3": 2}[groups[-2]]
									UpperCamelCase__       =							F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
									UpperCamelCase__       =							F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									UpperCamelCase__       =							prefix + resnet_block
									UpperCamelCase__       =							re_encoder_block_resnet.sub(_A					,  _A	)

						elif re_encoder_block_proj_out.fullmatch(_A	):
									UpperCamelCase__       =							re_encoder_block_proj_out.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
									UpperCamelCase__       =							re_encoder_block_proj_out.sub(_A					,  _A	)

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(_A	):
									UpperCamelCase__       =							re_decoder_block_conv_out.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[2]	) * 2 + int(groups[3]	) - 2
									UpperCamelCase__       =							F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
									UpperCamelCase__       =							re_decoder_block_conv_out.sub(_A					,  _A	)

						elif re_decoder_block_resnet.fullmatch(_A	):
									UpperCamelCase__       =							re_decoder_block_resnet.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[2]	) * 2 + int(groups[3]	) - 2
									UpperCamelCase__       =							{"1": 1, "3": 2}[groups[-2]]
									UpperCamelCase__       =							F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
									UpperCamelCase__       =							F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									UpperCamelCase__       =							prefix + resnet_block
									UpperCamelCase__       =							re_decoder_block_resnet.sub(_A					,  _A	)

						elif re_decoder_block_proj_in.fullmatch(_A	):
									UpperCamelCase__       =							re_decoder_block_proj_in.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
									UpperCamelCase__       =							re_decoder_block_proj_in.sub(_A					,  _A	)

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(_A	):
									UpperCamelCase__       =							re_prior_cond_conv_out.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[1]	) * 2 + int(groups[2]	) - 2
									UpperCamelCase__       =							F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
									UpperCamelCase__       =							re_prior_cond_conv_out.sub(_A					,  _A	)

						elif re_prior_cond_resnet.fullmatch(_A	):
									UpperCamelCase__       =							re_prior_cond_resnet.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							int(groups[1]	) * 2 + int(groups[2]	) - 2
									UpperCamelCase__       =							{"1": 1, "3": 2}[groups[-2]]
									UpperCamelCase__       =							F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
									UpperCamelCase__       =							F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									UpperCamelCase__       =							prefix + resnet_block
									UpperCamelCase__       =							re_prior_cond_resnet.sub(_A					,  _A	)

						elif re_prior_cond_proj_in.fullmatch(_A	):
									UpperCamelCase__       =							re_prior_cond_proj_in.match(_A	)
									UpperCamelCase__       =							regex_match.groups()
									UpperCamelCase__       =							F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
									UpperCamelCase__       =							re_prior_cond_proj_in.sub(_A					,  _A	)

						# keep original key
						else:
									UpperCamelCase__       =							original_key

						UpperCamelCase__       =							replace_key(_A	)

						if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
									print(F'''failed converting {original_key} to {key}, does not match'''	)

						# handle missmatched shape
						elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
									UpperCamelCase__       =							model_state_dict[F'''{key_prefix}.{key}''']
									print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''	)
									UpperCamelCase__       =							original_key

						UpperCamelCase__       =							original_key
						UpperCamelCase__       =							value

			return new_dict
@torch.no_grad()
def 							UpperCamelCase_(					_A :Any=None					,  _A :Any=None	)->		Dict:
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'	)[-1]}'''	):
									UpperCamelCase__       =							requests.get(F'''{PREFIX}{file}'''					,  allow_redirects=_A	)
									os.makedirs(F'''{pytorch_dump_folder_path}/'''					,  exist_ok=_A	)
									open(F'''{pytorch_dump_folder_path}/{file.split('/'	)[-1]}'''					,  "wb"	).write(r.content	)

			UpperCamelCase__       =							MODEL_MAPPING[model_name.split("/"	)[-1]]

			UpperCamelCase__       =							JukeboxConfig.from_pretrained(_A	)
			UpperCamelCase__       =							JukeboxModel(_A	)

			UpperCamelCase__       =							[]
			UpperCamelCase__       =							{}
			for i, dict_name in enumerate(_A	):
						UpperCamelCase__       =							torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'	)[-1]}'''	)["model"]

						UpperCamelCase__       =							{}
						for k in old_dic.keys():
									if k.endswith(".b"	):
												UpperCamelCase__       =							old_dic[k]
									elif k.endswith(".w"	):
												UpperCamelCase__       =							old_dic[k]
									elif "level_2" not in dict_name and "cond.model." in k:
												UpperCamelCase__       =							old_dic[k]
									else:
												UpperCamelCase__       =							old_dic[k]

						UpperCamelCase__       =							"vqvae" if i == 0 else F'''priors.{3 - i}'''
						UpperCamelCase__       =							fix_jukebox_keys(_A					,  model.state_dict()					,  _A					,  _A	)
						weight_dict.append(_A	)

			UpperCamelCase__       =							weight_dict.pop(0	)
			model.vqvae.load_state_dict(_A	)
			for i in range(len(_A	)	):
						model.priors[i].load_state_dict(weight_dict[2 - i]	)

			Path(_A	).mkdir(exist_ok=_A	)
			with open(F'''{pytorch_dump_folder_path}/mapping.json'''					,  "w"	) as txtfile:
						json.dump(_A					,  _A	)

			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
			model.save_pretrained(_A	)

			return weight_dict


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='jukebox-5b-lyrics',
						    type=str,
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='jukebox-5b-lyrics-converted',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						__UpperCamelCase     		=							parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


